#!/usr/bin/env python
# coding=utf-8
#!/usr/bin/env python
# coding=utf-8
# Copyright The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import sys
sys.path.append('.')
import argparse
import functools
import gc
import itertools
import json
import logging
import math
import os
import random
import shutil
from pathlib import Path
from typing import List, Union

import accelerate
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torchvision.transforms.functional as TF
import transformers
import webdataset as wds
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from braceexpand import braceexpand
from huggingface_hub import create_repo
from packaging import version
from peft import LoraConfig, get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict
from torch.utils.data import default_collate
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, CLIPTextModel, PretrainedConfig

import diffusers

from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
from opensora.registry import DATASETS, MODELS, SCHEDULERS, build_module
from opensora.acceleration.parallel_states import (
    get_data_parallel_group,
    set_data_parallel_group,
    set_sequence_parallel_group,
)
import torch.distributed as dist
from torch.distributions import LogisticNormal, Uniform
from opensora.datasets import prepare_dataloader
MAX_SEQ_LENGTH = 77
from opensora.models.text_encoder.t5 import text_preprocessing
from torch.utils.data import DataLoader, Dataset
if is_wandb_available():
    import wandb
from opensora.datasets import IMG_FPS, save_sample
import time
# import deepspeed
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.18.0.dev0")

logger = get_logger(__name__)

#multi resolution

def timestep_transform(
    t,
    model_kwargs,
    base_resolution=512 * 512,
    base_num_frames=1,
    scale=1.0,
    num_timesteps=1,
):
    t = t / num_timesteps
    
    resolution = model_kwargs["height"][0] * model_kwargs["width"][0]
    ratio_space = (resolution / base_resolution).sqrt()
    # NOTE: currently, we do not take fps into account
    # NOTE: temporal_reduction is hardcoded, this should be equal to the temporal reduction factor of the vae
    if model_kwargs["num_frames"][0] == 1:
        num_frames = torch.ones_like(model_kwargs["num_frames"])
    else:
        num_frames = model_kwargs["num_frames"][0] // 17 * 5
    ratio_time = (num_frames / base_num_frames).sqrt()

    ratio = ratio_space * ratio_time * scale
    
    new_t = ratio * t / (1 + (ratio - 1) * t)
    
    new_t = new_t * num_timesteps
    
    return new_t



def log_validation(vae, dit, args, accelerator, weight_dtype, step, text_encoder, model_args, mode):
    logger.info("Running validation... ")
    # sample_steps = [5]
    ar_table = [(17, 256, 256), (17,720,1280),(51,256,256), (51, 720, 1280)]
    # scheduler_table = []
    
    scheduler_config = dict(
        type="rflow",
        use_timestep_transform=True,
        num_sampling_steps=5,
        cfg_scale=7.0,
    )
        # scheduler_table.append(build_module(scheduler_config, SCHEDULERS))
    scheduler = build_module(scheduler_config, SCHEDULERS) 
    model = accelerator.unwrap_model(dit)
    
    text_encoder.y_embedder = model.y_embedder 
    model_args_copy = model_args.copy()
    del model_args_copy["y"]

    if args.seed is None:
        generator = None
    else:
        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)

    # validation_prompts = [
    #     "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
    #     "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
    #     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    #     "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
    # ]
    validation_prompts = [
    #    "several tall pine trees on the snow covered mountain peak",
       #"boundless sea fulls of surging waves and mixed with wind and rain",

    #    "sunny and sunny coast, waves wave after wave hit the beach",
        "a clear stream flows slowly in the quiet forest. aesthetic score : 6.5"
    ]

    for i, prompt in enumerate(validation_prompts):
        with torch.no_grad():
            video_51_frames = []
            video_17_frames = []
            with torch.autocast("cuda", dtype=weight_dtype):
                for j, ar in enumerate(ar_table):
                    latent_size = vae.get_latent_size(ar)
                    T, H, W = ar
                    z = torch.randn(1, vae.out_channels, *latent_size, device=accelerator.device, dtype=weight_dtype)
                    model_args_copy["height"] = torch.tensor([H], device=accelerator.device).repeat(1)
                    model_args_copy["width"]  = torch.tensor([W], device=accelerator.device).repeat(1)
                    model_args_copy["num_frames"] = torch.tensor([T], device=accelerator.device).repeat(1)
                    model_args_copy["fps"] = torch.tensor([24], device=accelerator.device).repeat(1)
                    model_args_copy["ar"] = torch.tensor([H/W], device=accelerator.device).repeat(1)
                    samples = scheduler.no_cfg_sample(
                        model,
                        text_encoder,
                        z=z,
                        prompts = [text_preprocessing(prompt)],
                        device=accelerator.device,
                        additional_args = model_args_copy,
                        # num_of_inference_step = sample_step
                    )
                    vidoes = vae.decode(samples.to(weight_dtype))
                    if not os.path.exists(os.path.join(args.output_dir, f"sample_change/{mode}/{step}/4_step")):
                        os.makedirs(os.path.join(args.output_dir, f"sample_change/{mode}/{step}/4_step"))
                    save_sample(vidoes[0], save_path=os.path.join(args.output_dir, f"sample_change/{mode}/{step}/4_step/{i}"))
        torch.cuda.empty_cache()

@torch.no_grad()
def update_ema(target_params, source_params, rate=0.99):
    """
    Update target parameters to be closer to those of source parameters using
    an exponential moving average.

    :param target_params: the target parameter sequence.
    :param source_params: the source parameter sequence.
    :param rate: the EMA rate (closer to 1 means slower).
    """
    for targ, src in zip(target_params, source_params):
        targ.detach().mul_(rate).add_(src, alpha=1 - rate)

def sample_t(x, loc = 0.0, scale = 1.0):
    distribution = LogisticNormal(torch.tensor([loc]), torch.tensor([scale]))
    t = distribution.sample((x.shape[0],))[:, 0].to(x.device) * 1000
    return t

def sample_s_from_t(t, stage, q=15):
    
    # decay = 1 / q ** (4 - stage)
    # ratio = 1 - decay
    r = t - (stage + 1) * q
    return torch.clamp(r, min=0)



def parse_args():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    # ----------Model Checkpoint Loading Arguments----------
    parser.add_argument(
        "--pretrained_teacher_model",
        type=str,
        default=None,
        required=False,
        help="Path to pretrained LDM teacher model or model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--pretrained_vae_model_name_or_path",
        type=str,
        default=None,
        help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
    )
    parser.add_argument(
        "--teacher_revision",
        type=str,
        default=None,
        required=False,
        help="Revision of pretrained LDM teacher model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--revision",
        type=str,
        default=None,
        required=False,
        help="Revision of pretrained LDM model identifier from huggingface.co/models.",
    )
    # ----------Training Arguments----------
    # ----General Training Arguments----
    parser.add_argument(
        "--output_dir",
        type=str,
        default="/g0022010zlc/zyz/opensora_distill/checkpoints/continue_long_hig_res_train_uniform_not_use_timetransform",
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    parser.add_argument(
        "--cache_dir",
        type=str,
        default=None,
        help="The directory where the downloaded models and datasets will be stored.",
    )
    parser.add_argument("--seed", type=int, default=123, help="A seed for reproducible training.")
    # ----Logging----
    parser.add_argument(
        "--logging_dir",
        type=str,
        default="logs",
        help=(
            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
        ),
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="tensorboard",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
        ),
    )
    # ----Checkpointing----
    parser.add_argument(
        "--checkpointing_steps",
        type=int,
        default=500,
        help=(
            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
            " training using `--resume_from_checkpoint`."
        ),
    )
    parser.add_argument(
        "--checkpoints_total_limit",
        type=int,
        default=None,
        help=("Max number of checkpoints to store."),
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help=(
            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
        ),
    )
    # ----Image Processing----
    parser.add_argument(
        "--train_shards_path_or_url",
        type=str,
        default=None,
        help=(
            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
            " or to a folder containing files that 🤗 Datasets can understand."
        ),
    )
    parser.add_argument(
        "--resolution",
        type=int,
        default=256,
        help=(
            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
            " resolution"
        ),
    )
    parser.add_argument(
        "--center_crop",
        default=False,
        action="store_true",
        help=(
            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
            " cropped. The images will be resized to the resolution first before cropping."
        ),
    )
    parser.add_argument(
        "--random_flip",
        action="store_true",
        help="whether to randomly flip images horizontally",
    )
    # ----Dataloader----
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=0,
        help=(
            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
        ),
    )
    # ----Batch Size and Training Steps----
    parser.add_argument(
        "--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
    )
    parser.add_argument("--num_train_epochs", type=int, default=3)
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=8000,
        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--max_train_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        ),
    )
    # ----Learning Rate----
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=1e-6,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        default=False,
        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
    )
    parser.add_argument(
        "--lr_scheduler",
        type=str,
        default="constant",
        help=(
            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
            ' "constant", "constant_with_warmup"]'
        ),
    )
    parser.add_argument(
        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    # ----Optimizer (Adam)----
    parser.add_argument(
        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
    )
    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
    parser.add_argument("--adam_epsilon", type=float, default=1e-15, help="Epsilon value for the Adam optimizer")
    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
    # ----Diffusion Training Arguments----
    parser.add_argument(
        "--proportion_empty_prompts",
        type=float,
        default=0,
        help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
    )
    # ----Latent Consistency Distillation (LCD) Specific Arguments----
    parser.add_argument(
        "--w_min",
        type=float,
        default=6.0,
        required=False,
        help=(
            "The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
            " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
            " compared to the original paper."
        ),
    )
    parser.add_argument(
        "--w_max",
        type=float,
        default=8.0,
        required=False,
        help=(
            "The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
            " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
            " compared to the original paper."
        ),
    )
    parser.add_argument(
        "--num_ddim_timesteps",
        type=int,
        default=50,
        help="The number of timesteps to use for DDIM sampling.",
    )
    parser.add_argument(
        "--loss_type",
        type=str,
        default="huber",
        choices=["l2", "huber"],
        help="The type of loss to use for the LCD loss.",
    )
    parser.add_argument(
        "--huber_c",
        type=float,
        default=0.01,
        help="The huber loss parameter. Only used if `--loss_type=huber`.",
    )
    parser.add_argument(
        "--lora_rank",
        type=int,
        default=64,
        help="The rank of the LoRA projection matrix.",
    )
    # ----Mixed Precision----
    parser.add_argument(
        "--mixed_precision",
        type=str,
        default="bf16",
        choices=["no", "fp16", "bf16"],
        help=(
            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
        ),
    )
    parser.add_argument(
        "--allow_tf32",
        action="store_true",
        help=(
            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
        ),
    )
    parser.add_argument(
        "--cast_teacher_unet",
        action="store_true",
        help="Whether to cast the teacher U-Net to the precision specified by `--mixed_precision`.",
    )
    # ----Training Optimizations----
    parser.add_argument(
        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
    )
    parser.add_argument(
        "--gradient_checkpointing",
        action="store_true",
        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
    )
    # ----Distributed Training----
    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
    # ----------Validation Arguments----------
    parser.add_argument(
        "--validation_steps",
        type=int,
        default=500,
        help="Run validation every X steps.",
    )
    # ----------Huggingface Hub Arguments-----------
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--hub_model_id",
        type=str,
        default=None,
        help="The name of the repository to keep in sync with the local `output_dir`.",
    )
    # ----------Accelerate Arguments----------
    parser.add_argument(
        "--tracker_project_name",
        type=str,
        default="delta=30",
        help=(
            "The `project_name` argument passed to Accelerator.init_trackers for"
            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
        ),
    )

    args = parser.parse_args()
    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
    if env_local_rank != -1 and env_local_rank != args.local_rank:
        args.local_rank = env_local_rank

    if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")

    return args



def main(args):
    logging_dir = Path(args.output_dir, args.logging_dir)

    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
    # api_key = os.getenv("WANDB_API_KEY")
    # wandb.login(key=api_key)
    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
        #downcast_bf16=True,
        log_with=args.report_to,
        device_placement=True,
        project_config=accelerator_project_config,
        split_batches=False,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
    )

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        transformers.utils.logging.set_verbosity_warning()
        diffusers.utils.logging.set_verbosity_info()
    else:
        transformers.utils.logging.set_verbosity_error()
        diffusers.utils.logging.set_verbosity_error()
    # wandb.init(
    # # set the wandb project where this run will be logged
    #     project="shuffuling data training",

    #     # track hyperparameters and run metadata
    #     config={
    #         "learning_rate": 1e-6,
    #         "dataset": "opensoraplan + webvid",
    #         "training_steps": args.max_train_steps,
    #         "adamw episilon": 1e-15,
    #     }
    # )
    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)

        if args.push_to_hub:
            create_repo(
                repo_id=args.hub_model_id or Path(args.output_dir).name,
                exist_ok=True,
                token=args.hub_token,
                private=True,
            ).repo_id

    # 1. 创建采样scheduler.
    scheduler_config = dict(
        type="rflow",
        use_timestep_transform=False,
        sample_method="logit-normal",
        num_sampling_steps=1000,
        cfg_scale=7.0
    )
    noise_scheduler = build_module(scheduler_config, SCHEDULERS)

    # 2. 创建T5 text encoder
    text_encoder_config = dict(
        type="t5",
        from_pretrained="/g0022010zlc/zyz/Open-Sora-v1.2/weights/T5",
        cache_dir=None,  # "/mnt/hdd/cached_models",
        model_max_length=300,
    )
    text_encoder = build_module(text_encoder_config, MODELS)
    

    #3. 创建opensora vae
    vae_cfg = dict(
        type="OpenSoraVAE_V1_2",
        from_pretrained="/g0022010zlc/zyz/Open-Sora-v1.2/weights/OpenSora-VAE-v1.2",
        micro_frame_size=17,
        micro_batch_size=4,
    )
    vae = build_module(vae_cfg, MODELS)

    # 4. 构建多分辨率数据集 
    dataset_cfg = dict(
        type="VariableVideoTextDataset",
        data_path="/g0022010zlc/zyz/varia_aes_flow.csv",
        transform_name="resize_crop",
    )
    dataset = build_module(dataset_cfg, DATASETS)
    dataloader_args = dict(
        dataset=dataset,
        batch_size=args.train_batch_size,
        num_workers=8,
        seed=1024,
        shuffle=False,
        drop_last=True,
        pin_memory=True,
        accelerator=accelerator,
    )

    # 5. 创建多分辨率bucket
    # bucket_config = {
    #     "144p": {17: (1.0, 1), 33:(1.0, 1), 65:(1.0, 1)},
    #     "256": {17: (0.2, 1), 33: (0.5, 1), 65: (0.7, 1)},
    #     "240p": {17: (0.2, 1), 33: (0.5, 1), 65:(0.5, 1)},
    #     "360p": {17: (0.1, 1), 33: (0.3, 1), 65:(0.3, 1)},
    #     "512":{17: (0.05, 1), 33: (0.1, 1), 65: (0.0, None)},
    #     "480p":{17:(0.05, 1), 33: (0.1, 1), 65: (0.0, None)},
    #     "720p":{17:(0.05, 1), 33: (0.1, 1), 65: (0.0, None)}
    # }
    bucket_config = {
       "720p":{51:(1.0, args.train_batch_size)}
    }
    train_dataloader, sampler = prepare_dataloader(
        bucket_config=bucket_config,
        num_bucket_build_workers=8,
        **dataloader_args,
    )
    # 6. 创建学生DiT模型 
    student_cfg = dict(
        type="STDiT3-XL/2",
        from_pretrained="/g0022010zlc/zyz/webvid_and_osp",
        qk_norm=True,
        enable_flash_attn=True,
        enable_layernorm_kernel=True,
        freeze_y_embedder=True,
    )

    # 7. 创建教师DiT模型
    teacher_cfg = dict(
        type="STDiT3-XL/2",
        from_pretrained="/g0022010zlc/zyz/Open-Sora-v1.2/weights/OpenSora-STDiT-v3",
        qk_norm=True,
        enable_flash_attn=False,
        enable_layernorm_kernel=True,
        freeze_y_embedder=True,
    )
    input_size = (dataset.num_frames, *dataset.image_size)
    latent_size = vae.get_latent_size(input_size)
    teacher_dit = build_module(
        teacher_cfg,
        MODELS,
        input_size=latent_size,
        in_channels=vae.out_channels,
        caption_channels=text_encoder.output_dim,
        model_max_length=text_encoder.model_max_length
    )
    target_dit = build_module(
        student_cfg,
        MODELS,
        input_size=latent_size,
        in_channels=vae.out_channels,
        caption_channels=text_encoder.output_dim,
        model_max_length=text_encoder.model_max_length
    )
    
    # 7. Freeze teacher vae, text_encoder, and teacher dit
    vae.requires_grad_(False)
    text_encoder.t5.model.requires_grad_(False)
    teacher_dit.requires_grad_(False)
    target_dit.train()
    target_dit.requires_grad_(False)
    
    # 8. Create online (`dit`) student DiT.
    dit = build_module(
        student_cfg,
        MODELS,
        input_size=latent_size,
        in_channels=vae.out_channels,
        caption_channels=text_encoder.output_dim,
        model_max_length=text_encoder.model_max_length,
        grad_checkpointing=True
    )
    dit.train()
    

    # dit.requires_grad_(False)
    # for name, param  in dit.named_parameters():
    #     if param.requires_grad == True:
    #         print(name)
    # exit()

    # Check that all trainable models are in full precision
    low_precision_error_string = (
        " Please make sure to always have all model weights in full float32 precision when starting training - even if"
        " doing mixed precision training, copy of the weights should still be float32."
    )

    if accelerator.unwrap_model(dit).dtype != torch.float32:
        raise ValueError(
            f"Controlnet loaded as datatype {accelerator.unwrap_model(dit).dtype}. {low_precision_error_string}"
        )

  
    

    # 9. 混合精度训练，使用bf16
    # For mixed precision training we cast all non-trainable weigths to half-precision
    # as these weights are only used for inference, keeping weights in full precision is not required.
    weight_dtype = torch.float32
    if accelerator.mixed_precision == "fp16":
        weight_dtype = torch.float16
    elif accelerator.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16

 
    vae.to(accelerator.device, dtype=torch.float32)
    # if args.pretrained_vae_model_name_or_path is not None:
    #     vae.to(dtype=weight_dtype)
    text_encoder.t5.model.to(accelerator.device, dtype=torch.float32)

    # Move teacher_unet to device, cast to weight_dtype
    teacher_dit.to(accelerator.device, dtype=torch.float32)
    target_dit.to(accelerator.device, dtype=torch.float32)
    dit.to(accelerator.device, dtype=torch.float32)
   
    
  

    # 10. 保存模型，save 和load的hook函数
    # `accelerate` 0.16.0 will have better support for customized saving
    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
        def save_model_hook(models, weights, output_dir):
            if accelerator.is_main_process:
                target_dit.save_pretrained(os.path.join(output_dir, "model"))
                for _, model in enumerate(models):
                    # make sure to pop weight so that corresponding model is not saved again
                    if weights:  # 检查weights列表是否为空
                        weights.pop()
                    else:
                        print("Warning: weights list is empty, cannot pop.") 

        def load_model_hook(models, input_dir):
            from opensora.utils.ckpt_utils import load_checkpoint
            print("---"*5, input_dir)
            load_checkpoint(target_dit, input_dir)
            load_checkpoint(dit, input_dir) 
            for _ in range(len(models)):
                # pop models so that they are not loaded again
                models.pop()

        accelerator.register_save_state_pre_hook(save_model_hook)
        accelerator.register_load_state_pre_hook(load_model_hook)

    # 11. flash attention
    if args.enable_xformers_memory_efficient_attention:
        if is_xformers_available():
            import xformers

            xformers_version = version.parse(xformers.__version__)
            if xformers_version == version.parse("0.0.16"):
                logger.warn(
                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
                )
            dit.enable_xformers_memory_efficient_attention()
            teacher_dit.enable_xformers_memory_efficient_attention()
            # target_unet.enable_xformers_memory_efficient_attention()
        else:
            raise ValueError("xformers is not available. Make sure it is installed correctly")

    # Enable TF32 for faster training on Ampere GPUs,
    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
    if args.allow_tf32:
        torch.backends.cuda.matmul.allow_tf32 = True

    if args.gradient_checkpointing:
        dit.enable_gradient_checkpointing()

    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
    if args.use_8bit_adam:
        try:
            import bitsandbytes as bnb
        except ImportError:
            raise ImportError(
                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
            )

        optimizer_class = bnb.optim.AdamW8bit
    else:
        optimizer_class = torch.optim.AdamW

    # 12. Optimizer creation
    optimizer = optimizer_class(
        dit.parameters(),
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )

  
    

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer,
        num_warmup_steps=args.lr_warmup_steps,
        num_training_steps=args.max_train_steps,
    )
    text_encoder.y_embedder = dit.y_embedder

    # Prepare everything with our `accelerator`.
    
    dit, optimizer, lr_scheduler, train_dataloader = accelerator.prepare(dit, optimizer, lr_scheduler, train_dataloader)

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    #num_update_steps_per_epoch = math.ceil((len(dataset) / train_dataloader.batch_size) / args.gradient_accumulation_steps)
    num_update_steps_per_epoch = math.ceil((len(dataset) / args.train_batch_size) / args.gradient_accumulation_steps)
    # print(num_update_steps_per_epoch, len(dataset))
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initializes automatically on the main process.
    if accelerator.is_main_process:
        tracker_config = dict(vars(args))
        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)

   
    uncond_prompt_embeds = text_encoder.encode([""] * (args.train_batch_size))["y"]
    #print("uncond prompt embeds:", uncond_prompt_embeds.shape, "num gpu:", num_of_gpu)

    # Train!
    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num batches each epoch = {len(dataset) / args.train_batch_size}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  accelerate num processes = {accelerator.num_processes}")
    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")

    

    stage_one_epoch = 1000 
    global_step = 0
    first_epoch = 0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint != "latest":
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            # Get the most recent checkpoint
            dirs = os.listdir(args.output_dir)
            dirs = [d for d in dirs if d.startswith("checkpoint")]
            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
            path = dirs[-1] if len(dirs) > 0 else None

        if path is None:
            accelerator.print(
                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
            )
            args.resume_from_checkpoint = None
            initial_global_step = 0
        else:
            accelerator.print(f"Resuming from checkpoint {path}")
            accelerator.load_state(os.path.join(args.output_dir, path))
            global_step = int(path.split("-")[1])

            initial_global_step = global_step
            first_epoch = global_step // num_update_steps_per_epoch
    else:
        initial_global_step = 0

    progress_bar = tqdm(
        range(0, args.max_train_steps),
        initial=initial_global_step,
        desc="Steps",
        # Only show the progress bar once on each machine.
        disable=not accelerator.is_local_main_process,
    )
    #LogisticNormal采样
    # distribution = LogisticNormal(torch.tensor([0.0]), torch.tensor([1.0]))
    distribution = Uniform(low=torch.tensor([0.0]), high=torch.tensor([1.0]))
    # global_step = 0
    for epoch in range(first_epoch, args.num_train_epochs):
        for step, batch in enumerate(train_dataloader):
            with accelerator.accumulate(dit):
                video = batch.pop('video').to(accelerator.device, weight_dtype)
                text = batch.pop('text')
                flow = batch.pop("flow").cpu().numpy()
                aes = batch.pop("aes").cpu().numpy()
                # print(video.shape)
                B, C, T, H, W = video.shape
                batch_size = B
                model_args = dict()
                model_args["height"] = torch.tensor([H], device=accelerator.device).repeat(batch_size)
                model_args["width"]  = torch.tensor([W], device=accelerator.device).repeat(batch_size)
                model_args["num_frames"] = torch.tensor([T], device=accelerator.device).repeat(batch_size)
                model_args["ar"]  = torch.tensor([H / W], device=accelerator.device).repeat(batch_size)
                model_args["fps"] = batch.pop("fps").to(accelerator.device, weight_dtype)
                print(model_args['height'], model_args['width'], model_args['num_frames'])
                texts = []
                for index, t in enumerate(text):
                    texts.append(f"{t} aesthetic score : {aes[index]:.1f}. motion score: {flow[index]:.1f}")
                video = video.to(accelerator.device, non_blocking=True)
                
                if vae.dtype != weight_dtype:
                    vae.to(dtype=weight_dtype)
                
                latents = vae.encode(video)
                bsz = B
                latents = latents.to(weight_dtype)
                #随机采一个时间t
                t = distribution.sample((B,))[:, 0].to(latents.device) * 1000
               
                stage = (global_step // stage_one_epoch) + 1
                # 多分辨率微调阶段，stage固定为6
                # stage = 6
                delta_t = torch.tensor([stage * 30]*bsz).to(latents.device)
                while torch.any(t < delta_t):
                    t = distribution.sample((latents.shape[0],))[:, 0].to(latents.device) * 1000 
                s = t - delta_t
                #根据分辨率转换t
                # t_transformed = torch.tensor([timestep_transform(timesteps, model_args, num_timesteps=1000) for timesteps in t]).to(latents.device)
                # s_transformed =  torch.tensor([timestep_transform(timesteps, model_args, num_timesteps=1000) for timesteps in s]).to(latents.device)
                # delta_t_transformed = t_transformed - s_transformed

                #采样噪声
                noise =torch.randn_like(latents)
                noisy_model_input = noise_scheduler.scheduler.add_noise(latents, noise, t)

                #随机选一个guidance scale
                w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min
                w = w.reshape(bsz, 1, 1, 1, 1)
                w = w.to(device=latents.device, dtype=latents.dtype)
                w = w.reshape(bsz, 1, 1, 1, 1)
                w = w.to(device=latents.device, dtype=latents.dtype)

                #编码prompt
                batch_prompts = [text_preprocessing(t) for t in texts]
                batch_prompts_embed = text_encoder.encode(batch_prompts)["y"] 
                model_args["y"] = batch_prompts_embed

                #online模型预测速度
                v_pred_student = dit(noisy_model_input, t, **model_args)
                #print(v_pred_student, t_transformed, noisy_model_input)
                B, C = v_pred_student.shape[:2]
                v_pred_student, _ = torch.split(v_pred_student, C // 2, dim=1)
                #计算教师模型预测的平均速度，每三十步预测一次
                x_i = noisy_model_input
                last_t = t
                with torch.no_grad():
                    for i in range(1, stage + 1):
                        t_i = t - i * 30
                        t_i = torch.clamp(t_i, min=0)
                        # t_i_transformed =  torch.tensor([timestep_transform(timesteps, model_args, num_timesteps=1000) for timesteps in t_i]).to(latents.device)
                       
                        model_args["y"] = batch_prompts_embed
                        v_pred_teacher_cond = teacher_dit(x_i, t_i, **model_args)
                        model_args["y"] = uncond_prompt_embeds
                        v_pred_teacher_uncond = teacher_dit(x_i, t_i, **model_args)
                        #教师模型预测的速度是使用cfg的 
                        v_pred_teacher = v_pred_teacher_uncond + w * (v_pred_teacher_cond - v_pred_teacher_uncond) 
                        v_pred_teacher, _ = torch.split(v_pred_teacher, C // 2, dim=1)
                        x_i = x_i + (last_t - t_i).view(-1, 1,1,1,1) / 1000 * v_pred_teacher
                        last_t = t_i
                teacher_accumulate = (x_i - noisy_model_input)*1000 / delta_t.view(-1,1,1,1,1)


                student_accumulate = v_pred_student

                #计算loss，对齐学生模型预测速度与教师模型预测的平均速度
                loss = torch.mean(torch.sqrt((student_accumulate.float()  - teacher_accumulate.float()) ** 2 + args.huber_c**2) - args.huber_c)
                accelerator.backward(loss)
                if accelerator.sync_gradients:
                    accelerator.clip_grad_norm_(dit.parameters(), args.max_grad_norm)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad(set_to_none=True)

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                update_ema(target_dit.parameters(), dit.parameters(), 0.95)
                progress_bar.update(1)
                global_step += 1

                if accelerator.is_main_process:
                    if global_step % args.checkpointing_steps == 0:
                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
                        if args.checkpoints_total_limit is not None:
                            checkpoints = os.listdir(args.output_dir)
                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))

                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
                            if len(checkpoints) >= args.checkpoints_total_limit:
                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
                                removing_checkpoints = checkpoints[0:num_to_remove]

                                logger.info(
                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
                                )
                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")

                                for removing_checkpoint in removing_checkpoints:
                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
                                    shutil.rmtree(removing_checkpoint)

                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
                        accelerator.save_state(save_path)
                        logger.info(f"Saved state to {save_path}")

                    if global_step % args.validation_steps == 0:
                        log_validation(vae, target_dit, args, accelerator, weight_dtype, global_step, text_encoder,  model_args, mode="offline")
                        log_validation(vae, dit, args, accelerator, weight_dtype, global_step, text_encoder,  model_args, mode="online")
            del model_args["y"]
            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "stage" : stage, "delta_t": delta_t[0].detach().item()}
            progress_bar.set_postfix(**logs)
            accelerator.log(logs, step=global_step)
            # wandb.log(logs)

            if global_step >= args.max_train_steps:
                break


    # Create the pipeline using using the trained modules and save it.
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        dit = accelerator.unwrap_model(dit)
        dit.save_pretrained(args.output_dir)
    accelerator.end_training()


if __name__ == "__main__":
    args = parse_args()
    main(args)


